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AI / LLM.

practical intelligence in real software

Not demos — production features. Document extraction, assistants, smart automation, and retrieval over your own business data, built into the software you already run. The hard part of AI isn't calling the model; it's making the result reliable enough to trust with real business data: validation, guardrails, human review, cost control, and audit trails. That's the part I focus on.

Role in my stack
Product features
Built into
Laravel platforms
Best for
Extraction & automation
Principle
Reliability first
Where it shines

Document extraction

Invoices, contracts, and forms read automatically into your system — hours of manual entry become minutes of review, with fewer errors.

Assistants & copilots

Chat and task assistants grounded in your data with strict guardrails — helpful answers, never invented ones.

Smart automation

Classification, routing, summarizing, drafting — repetitive judgment calls handled automatically, with human review exactly where it matters.

RAG over your data

Vector search and retrieval that lets AI answer from your documents and records — accurately, traceably, with sources shown.

Ecosystem & tools I use with it
Claude / GPT APIsEmbeddingsVector search (pgvector)RAG pipelinesStructured outputsPrompt evaluationLaravel queuesCost monitoringHuman-in-the-loopAudit logging
When I reach for it
01
Manual data entry anywhere

If staff retype documents into systems, extraction almost always pays for itself within months.

02
Repetitive decisions at volume

Triage, categorization, first-draft responses — AI handles the bulk, people handle the edge cases.

03
Knowledge locked in documents

When the answer exists somewhere in your files, retrieval-augmented search finds it in seconds.

04
Features that were impossible before

Summarizing case histories, drafting in your tone, multilingual support on the fly — capabilities that simply didn't exist a few years ago.

How it fits together

AI features live inside the platform, not beside it: every model call runs through queues with retries, outputs are validated against business rules before touching your data, and every decision is logged so you can always answer 'why did it do that?'

Structured outputs + validation: the model fills a strict schema, and anything doubtful goes to human review.
Cost caps and monitoring per feature — you always know what the AI spend is and why.
Model-agnostic design: providers are swappable as pricing and quality shift — no lock-in.
Straight answers what clients actually ask
"Can we trust AI with our business data?"

With the right architecture, yes: validation before writes, human review for low-confidence cases, EU data processing options, and full audit trails. Trust is designed, not assumed.

"What does it cost to run?"

Typically far less than the manual work it replaces — extraction often runs at cents per document. I build cost monitoring in, so there are no surprise bills.

"Will it hallucinate at our customers?"

Not if it's built right: grounding in your data, strict output schemas, and 'I don't know' as an allowed answer beat any clever prompt.

Work built with AI / LLM
AI-assisted property inspection with media analysis and generated findings a property inspection platform
Official www.anthropic.com ↗ Documentation ↗

In my work

My AI work ships inside Laravel platforms with validation, cost caps, and audit trails — hours of manual document entry turned into minutes of review.

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